Sharing personal ECG time-series data privately
Sharing personal ECG time-series data privately
复制标题
私下共享个人心电图时间序列数据
DOI：
10.1093/jamia/ocac047
复制
发表时间：
2022
影响因子：
6.4
通讯作者：
Fan, Liyue
中科院分区：
文献类型：
--
作者：
Bonomi, Luca;Wu, Zeyun;Fan, Liyue
目的应急技术(如可穿戴设备)使直接从个人(如时间序列)收集数据成为可能，为了解单个患者的健康和福祉提供了新的见解。扩大对这些数据的访问将有助于与现有数据源(如临床和基因组数据)的整合，并促进医学研究。与传统的健康数据相比，这些数据直接从个人收集，具有高度的独特性，并提供细粒度的信息，给隐私带来了新的挑战。本文研究了一种新的隐私模型在保持数据分析可用性的同时实现个体级时间序列数据共享的适用性。方法和材料提出了一种共享个体级心电时间序列数据的隐私保护方法，该方法利用降维技术和随机抽样来实现可证明的隐私保护。我们的解决方案在提供有用的聚合分析的同时，针对知情的对抗模型提供了强大的隐私保护。我们的实验结果表明，消毒后的隐私风险显著降低，同时保持了数据对于各种临床任务(如预测建模和聚类)的可用性。讨论我们的研究调查了共享个体水平的心电时间序列数据时的隐私风险。我们证明，个人级别的数据可以是高度独特的，需要新的隐私解决方案来保护数据贡献者。结论结果表明，我们提出的隐私保护方法在保留数据有用性的同时，提供了强大的隐私保护。
ObjectiveEmerging technologies (eg, wearable devices) have made it possible to collect data directly from individuals (eg, time-series), providing new insights on the health and well-being of individual patients. Broadening the access to these data would facilitate the integration with existing data sources (eg, clinical and genomic data) and advance medical research. Compared to traditional health data, these data are collected directly from individuals, are highly unique and provide fine-grained information, posing new privacy challenges. In this work, we study the applicability of a novel privacy model to enable individual-level time-series data sharing while maintaining the usability for data analytics.Methods and materialsWe propose a privacy-protecting method for sharing individual-level electrocardiography (ECG) time-series data, which leverages dimensional reduction technique and random sampling to achieve provable privacy protection. We show that our solution provides strong privacy protection against an informed adversarial model while enabling useful aggregate-level analysis.ResultsWe conduct our evaluations on 2 real-world ECG datasets. Our empirical results show that the privacy risk is significantly reduced after sanitization while the data usability is retained for a variety of clinical tasks (eg, predictive modeling and clustering).DiscussionOur study investigates the privacy risk in sharing individual-level ECG time-series data. We demonstrate that individual-level data can be highly unique, requiring new privacy solutions to protect data contributors.ConclusionThe results suggest our proposed privacy-protection method provides strong privacy protections while preserving the usefulness of the data.
DOI：
10.1056/nejmsr1809937
发表时间：
2019-08-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
All of Us Research Program Investigators;Denny JC;Rutter JL;Goldstein DB;Philippakis A;Smoller JW;Jenkins G;Dishman E
通讯作者：
Dishman E
影响因子：
1.8
作者：
Kifer, Daniel;Machanavajjhala, Ashwin
通讯作者：
Machanavajjhala, Ashwin
影响因子：
--
作者：
Sufi, Fahim;Khalil, Ibrahim
通讯作者：
Khalil, Ibrahim